scholarly journals PEMBANGKITAN DATA UJI BERBASIS DIAGRAM AKTIVITAS DAN DIAGRAM STATECHART

JOUTICA ◽  
2018 ◽  
Vol 3 (1) ◽  
pp. 137
Author(s):  
Arif Rahman Sujatmika ◽  
Yanuangga Gala Hartlambang
Keyword(s):  
Test Data ◽  
Test Case ◽  
Test Cases ◽  
Data Dictionary ◽  
Activity Diagram ◽  
Statechart Diagram ◽  
The Status ◽  
Generate Test Data ◽  
Case Data ◽  
Selection Of

Testing is the stage of software development used to determine whether a software is ready for release or not. In making test cases using reference activity diagrams and statechart diagrams, a help representation was made, ie State-Activity-Diagram (SAD). The generation of test cases using a reference between the statechart diagram and the status diagram is still inadequate because in the case of the test produced there is no test data. The selection of test data for many test cases will be tedious and time consuming. In this paper, it is proposed to generate test data automatically based on existing test cases. Test data created based on class diagrams, and data dictionaries. The test case data consists of inputs and results. First enter information about the functions involved in the test case into the SAD node so that the SAD-S Diagram is obtained. Second, after the process of making the test case is completed, the test data is made by looking at the data dictionary function so that the test data is formed.

Cost and Effectiveness of Search-Based Techniques for Model-Based Testing: An Empirical Analysis

2017 ◽  
Vol 27 (04) ◽  
pp. 601-622 ◽  
Author(s):  
Aneesa Saeed ◽  
Siti Hafizah Ab Hamid ◽  
Asmiza Abdul Sani
Keyword(s):  
Test Data ◽  
Random Search ◽  
Test Cases ◽  
Optimal Test ◽  
Model Driven ◽  
Model Based ◽  
Model Based Testing ◽  
Generate Test Data ◽  
The Cost ◽  
Model Driven Approach

Model-based testing (MBT) seems to be gaining interest in industry and academia due to its provision of systematic, automated and comprehensive testing. The challenge in MBT is to generate optimal test data to execute test cases. Recently, researchers have successfully applied search-based techniques (SBTs) by automating the search for an optimal set of test data at reasonable cost compared to other more expensive techniques. In real complex systems, effectiveness and cost of SBTs for MBT in industrial context are little known. The objective of this study is to empirically evaluate the cost and the effectiveness of SBTs for MBT on industrial case studies. We applied a model-driven approach and SBTs to automatically generate executable feasible test cases. The results show that the model-driven approach generated high number of infeasible test cases with less time while genetic algorithm (GA) and simulating annealing (SA) outperformed significantly random search (RS) with high generation time. We concluded that local SBTs are more appropriate to generate test data when the type of the constraints is simple. Current work on analyzing the cost and effectiveness on SBTs for MBT indicates possible enhancement using the model-driven approach to detect the infeasible paths and SBTs to achieve optimal success rate.

A Analysis and Research on the Methods of Object Oriented Software Testing

2013 ◽  
Vol 709 ◽  
pp. 616-619
Author(s):  
Jing Chen
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Test Data ◽  
Object Oriented ◽  
Automatic Generation ◽  
Test Case Generation ◽  
Test Case ◽  
Test Cases ◽  
Path Coverage ◽  
Parameter Values

This paper proposes a genetic algorithm-based method to generate test cases. This method provides information for test case generation using state machine diagrams. Its feature is realizing automation through fewer generated test cases. In terms of automatic generation of test data based on path coverage, the goal is to build a function that can excellently assess the generated test data and guide the genetic algorithms to find the targeting parameter values.

A Software Testing-Progress Evaluation Model Based on a Digestion Process of Test-Cases

1997 ◽  
Vol 04 (03) ◽  
pp. 229-239 ◽  
Author(s):  
Mitsuhiro Kimura ◽  
Shigeru Yamada
Keyword(s):  
Software Testing ◽  
Large Scale ◽  
Evaluation Model ◽  
Test Case ◽  
Test Cases ◽  
Software Production ◽  
Proposed Model ◽  
Software Product ◽  
Software Engineers ◽  
Case Data

It is of great importance for software engineers and managers to evaluate software testing-progress in a large-scale software production process, since tremendous software development resources must be consumed to achieve high quality and reliability of a software product. By focusing on the behavior of the digested test-case data observed in the testing process, we construct a stochastic model and derive several quantitative measures for software testing-progress evaluation. Actual data observed in the testing process are analyzed by the proposed model, and we discuss the applicability of our models.

Test Case Selection: A Systematic Literature Review

2014 ◽  
Vol 24 (04) ◽  
pp. 653-676 ◽  
Author(s):  
Everton Note Narciso ◽  
Márcio Eduardo Delamaro ◽  
Fátima De Lourdes Dos Santos Nunes
Keyword(s):  
Resource Constraints ◽  
Test Case ◽  
Test Cases ◽  
Random Testing ◽  
Test Case Selection ◽  
Test Strategies ◽  
Case Selection ◽  
Definition Of ◽  
Selection Of ◽  
Leading Journals

Time and resource constraints should be taken into account in software testing activities, and thus optimizing the test suite is fundamental in the development process. In this context, the test case selection aims to eliminate redundant or unnecessary test data, which is crucial for the definition of test strategies. This paper presents a systematic review on the test case selection conducted through a selection of 449 articles published in leading journals and conferences in Computer Science. We addressed the state-of-art by collecting and comparing existing evidence on the methods used in the different software domains and the methods used to evaluate the test case selection. Our study identified 32 papers that met the research objectives, which featured 18 different selection methods and were evaluated through 71 case studies. The most commonly reported methods are adaptive random testing, genetic algorithms and greedy algorithm. Most approaches rely on heuristics, such as diversity of test cases and code or model coverage. This paper also discusses the key concepts and approaches, areas of application and evaluation metrics inherent to the methods of test case selection available in the literature.

scholarly journals Survey on Mutation-based Test Data Generation

2015 ◽  
Vol 5 (5) ◽  
pp. 1164
Author(s):  
Hanh Le Thi My ◽  
Binh Nguyen Thanh ◽  
Tung Khuat Thanh
Keyword(s):  
Test Data ◽  
Test Cases ◽  
Generation Process ◽  
Test Data Generation ◽  
Data Generation ◽  
Comprehensive Survey ◽  
Critical Activity ◽  
Automatic Test Data Generation ◽  
Systematic Selection ◽  
Selection Of

<span>The critical activity of testing is the systematic selection of suitable test cases, which be able to reveal highly the faults. Therefore, mutation coverage is an effective criterion for generating test data. Since the test data generation process is very labor intensive, time-consuming and error-prone when done manually, the automation of this process is highly aspired. The researches about automatic test data generation contributed a set of tools, approaches, development and empirical results. In this paper, we will analyse and conduct a comprehensive survey on generating test data based on mutation. The paper also analyses the trends in this field.</span>

scholarly journals Test Case Generation for UML Behavioral Diagram by Traversal Algorithm

2019 ◽  
Vol 8 (10) ◽  
pp. 3262-3266
Keyword(s):  
Accurate Result ◽  
Test Case Generation ◽  
Test Case ◽  
Test Cases ◽  
Sequence Diagrams ◽  
Research Focus ◽  
Activity Diagram ◽  
Path Coverage ◽  
The Comparative Study ◽  
Traversal Algorithm

Software testing play crucial role in the software development as it consumes lot of time and resources. However testing process needs to be more efficiently done because overall software quality relies upon good testing approach. The present research focus on generation of test cases from UML diagrams. The combination graph is made by using activity and sequence diagrams. These diagrams proves to be more efficient as activity diagram gives the dynamic behavior of the model and sequence diagram is used to understand detailed functionality of the system. In this paper, a combined approach using Breadth first and depth first search is proposed which will generate expected test cases. The comparative study is done for test case generation using BFS and DFS algorithm and the result proves that the DFS traversal algorithm provides more accurate result for path coverage.

USLTG: Test Case Automatic Generation by Transforming Use Cases

2019 ◽  
Vol 29 (09) ◽  
pp. 1313-1345 ◽  
Author(s):  
Chu Thi Minh Hue ◽  
Duc-Hanh Dang ◽  
Nguyen Ngoc Binh ◽  
Anh-Hoang Truong
Keyword(s):  
Test Data ◽  
Test Generation ◽  
Automatic Generation ◽  
Specification Language ◽  
Use Cases ◽  
Test Case ◽  
Test Cases ◽  
Generate Functional ◽  
Build System ◽  
Test Objects

This paper proposes a transformation-based method to automatically generate functional test cases from use cases named USLTG (Use case Specification Language (USL)-based Test Generation). We first focus on developing a modeling language named Test Case Specification Language (TCSL) in order to express test cases. Test cases in TCSL can contain detailed information including test steps, test objects within steps, actions of test objects, and test data. Such information is often ignored in currently available test case specifications. We then aim to generate test cases in a TCSL model by a transformation from use cases that are represented by a USL. The USLTG transformation includes three main steps in generating (1) scenarios, (2) test data, and (3) a TCSL model. Within our transformation, the OCL solver is employed in order to build system snapshots as the part of test cases and to identify other test data. We applied our method to two case studies and evaluated our method by comparing it with other recent works.

scholarly journals Mutated Specification-Based Test Data Generation with a Genetic Algorithm

Mathematics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 331
Author(s):  
Rong Wang ◽  
Yuji Sato ◽  
Shaoying Liu
Keyword(s):  
Genetic Algorithm ◽  
Test Data ◽  
Test Cases ◽  
Formal Specifications ◽  
Data Generation ◽  
Testing Methods ◽  
Functional Changes ◽  
Target Program ◽  
Generate Test Data

Specification-based testing methods generate test data without the knowledge of the structure of the program. However, the quality of these test data are not well ensured to detect bugs when non-functional changes are introduced to the program. To generate test data effectively, we propose a new method that combines formal specifications with the genetic algorithm (GA). In this method, formal specifications are reformed by GA in order to be used to generate input values that can kill as many mutants of the target program as possible. Two classic examples are presented to demonstrate how the method works. The result shows that the proposed method can help effectively generate test cases to kill the program mutants, which contributes to the further maintenance of software.

scholarly journals Automated Test Data Generation Based on a Genetic Algorithm with Maximum Code Coverage and Population Diversity

2021 ◽  
Vol 11 (10) ◽  
pp. 4673
Author(s):  
Tatiana Avdeenko ◽  
Konstantin Serdyukov
Keyword(s):  
Test Data ◽  
Fitness Function ◽  
Population Diversity ◽  
Test Case ◽  
Test Cases ◽  
Data Generation ◽  
Intelligent Support ◽  
Automated Generation ◽  
Code Coverage ◽  
Automated Test Data Generation

In the present paper, we investigate an approach to intelligent support of the software white-box testing process based on an evolutionary paradigm. As a part of this approach, we solve the urgent problem of automated generation of the optimal set of test data that provides maximum statement coverage of the code when it is used in the testing process. We propose the formulation of a fitness function containing two terms, and, accordingly, two versions for implementing genetic algorithms (GA). The first term of the fitness function is responsible for the complexity of the code statements executed on the path generated by the current individual test case (current set of statements). The second term formulates the maximum possible difference between the current set of statements and the set of statements covered by the remaining test cases in the population. Using only the first term does not make it possible to obtain 100 percent statement coverage by generated test cases in one population, and therefore implies repeated launch of the GA with changed weights of the code statements which requires recompiling the code under the test. By using both terms of the proposed fitness function, we obtain maximum statement coverage and population diversity in one launch of the GA. Optimal relation between the two terms of fitness function was obtained for two very different programs under testing.

scholarly journals MODEL BASED TEST CASE PRIORITIZATION USING UML ACTIVITY DIAGRAM AND EVOLUTIONARY ALGORITHM

2014 ◽  
pp. 76-81
Author(s):  
PRATEEVA MAHALI ◽  
ARUP ABHINNA ACHARYA
Keyword(s):  
Critical Path ◽  
Regression Testing ◽  
Test Case ◽  
Test Cases ◽  
Activity Diagram ◽  
Development Life Cycle ◽  
Software Development Life Cycle ◽  
Uml Activity Diagram ◽  
Independent Path ◽  
Time Required

With the exponential growth in size and complexity of softwares, the testing activity is no more limited to testing phase of SDLC (Software Development Life Cycle). Testing process has been made iterative and incremental in Object Oriented development scenario. This leads to increase in effort and time required for testing as well as explosion in test case. But when it comes to regression testing, it has the additional issue of test case retesting which further increasing the effort and time. So a suitable prioritization technique should be used to address these issues. In this paper we had given a proposal which is based on prioritization of test cases using GA (Genetic Algorithm). This process is found to be very effective during regression testing. In this paper we found an optimized independent path having maximum critical path value, which further leads to prioritization of test cases. The three component of regression testing i.e effort, time, cost will be gradually reduce by using this approach.

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